Mobile data and voice communication continues to evidence significant growth. With increasing popularity of data and voice communication, it is more likely that the communication needs of a large number of users must to be met which are all located within a small area, a case referred to as dense crowd scenario or dense cell population in the art. Typical examples include sport arenas or large office buildings.
In order to increase data transmission performance and reliability, the so-called multiple input and multiple-output (MIMO) technology may be used in wireless radio telecommunication for transmitting information between a base station and terminals of users. MIMO systems may use multiple send and receive antennas for wireless communication at a base station. The MIMO technology forms the basis for coding techniques which use the temporal as well as the spatial dimension for transmitting information. The enhanced coding provided in MIMO systems allows a quality and data rate of the wireless communication to be increased, which makes the use of MIMO techniques attractive.
In a massive MIMO system, the base station may include a large number of antennas, e.g. several tens or even in excess of one hundred antennas with associated transceiver circuitry. The extra antennas of the MIMO base station allow radio energy to be spatially focused which improves cell capacity and radiated energy efficiency.
In order to adapt the transmit signal at each individual antenna of the base station in accordance with the currently active terminals a base station logic needs information about radio channel properties between the terminals and the antennas. The channel properties depend on the relative position between the terminal and the plurality of antennas of the base station. The signal footprint detected at the plurality of antennas, which may be quantified by a footprint matrix, may vary depending on the direction and also the distance in which the terminal is located. The signal footprint may also vary depending on the environment, as signals may be reflected or scattered at objects in the environment of the terminal and the base station. The channel properties may be used for focusing energy of radio signals when transmitting signals downlink or when retrieving data from uplink signals.
The terminals transmit a training sequence as pilot signal. The training sequences need to be orthogonal in order for the base station to identify the configuration parameters for the plurality of antennas for each of the one of the terminals in conventional systems. Orthogonality may be achieved by using time division multiple access (TDMA), code division multiple access (CDMA) or frequency division multiple access (FDMA) technologies or a combination thereof.
For a MIMO system which uses time division duplex (TDD) orthogonality is attained in the time domain. Each terminal can transmit a pilot signal which can be received by the antennas and analyzed by the base station logic. In order to not interfere with each other, a certain time period can be assigned in each system frame where each terminal may transmit its pilot signal. A header of a conventional frame may include time slots in which the terminals may transmit their pilot signals. The remaining time slots of the conventional frame may be used for downlink (DL) and uplink (UL) data transmission.
Massive MIMO systems may be deployed in buildings such as office buildings, shopping malls, sport arenas or other areas in which a large density of users can occur. Such situations are also referred to as “dense crowd” or “dense cell population” in the art of cellular communication systems. In such environments a large number of terminals may be located in the cell served by the MIMO base station. The time required for the pilot signaling of the terminals in each frame may increase with the number of terminals. For a large number of terminals, the time required for all terminals to transmit their pilot signals may exceed the available pilot signaling time in each frame. While the pilot signaling time, i.e. the number of time slots allocated to pilot signaling, may be adjusted dynamically, the transmission of payload data would be negatively affected if the pilot signaling time in each frame was increased to much. Accordingly, there is an upper limit for the pilot signaling time in each frame beyond which the payload data transmission would be negatively affected.
With increasing popularity of the so-called internet of things (ITO), the number of machine-type communication terminals located in a cell is expected to increase. Examples for such machine-type communication terminals include sensors or other devices which only require small data rates but nevertheless may need to remain synchronized to a base station. The ITO increases the risk that there may be dense cell populations, with many of the devices requiring low data rates only.
Dense cell populations may create a risk that some users or even a large number of users may be overseen due to the lack of available time slots for pilot transmission. Some of the users that are overseen may be active and ready for a connection, whereas some of the users that are assigned the time slots for transmitting pilot signals may be in idle mode and do not need the connection. Smart scheduling techniques for allocating terminals to available time slots may mitigate such problems to some extent, but the conventional resource allocation schemes for pilot signaling may still not allow the base station to reliably see all terminals that may be ready for a connection when in dense cell population scenarios.